Programmable Nanophotonics for Deep Learning and Neuromorphic Computing

用于深度学习和神经形态计算的可编程纳米光子学

• 批准号: RGPIN-2018-05249
• 负责人: Shastri, Bhavin
• 金额: $ 2.4万
• 依托单位: Queen's University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=664913
• 关键词: Programmable; Nanophotonics; Deep; Learning; Neuromorphic

SYNOPSIS. The birth of computers shaped 20th century society and science. After decades of exponential improvement, the performance of von Neumann architectures in speed, efficiency, and generality, has begun to run into fundamental limits, as the shrinking of transistors reaches its physical limits. As a result, the gap between current computing capabilities and computing needs is widening. This insufficiency is apparent in problems involving complex systems, big data, or real-time requirements. Forays into unconventional (non-von Neumann) computing have only been partially successful due to the limitations in bandwidth and energy consumption posed by metal interconnects.******VISION & OBJECTIVES. The groundwork of the proposed program stems from my Banting Fellowship which coined the term Neuromorphic Photonics. The vision of my cross-disciplinary research is to advance the understanding of photonic physics for scalable information processing by unifying nanophotonic (i.e. optical) physics, neuromorphic (i.e. brain-inspired) architectures, and emerging technology (silicon photonics) platforms. The resulting photonic processors will have the potential to outperform state-of-the-art microelectronic processors in energy efficiency and computational speeds by seven- and four orders-of-magnitude, respectively. Scientific objectives include: 1) devices thrust—energy efficient (attoJoule/operation) photonic neurons with graphene-based electro-optic modulators; 2) architectures thrust—scalable and programmable silicon photonic neural network architectures; and 3) applications thrust—photonic processors for generalized neuromorphic computing tasks including deep learning and nonlinear optimization for model predictive control. This program focuses on these computing tasks as they are notoriously difficult to solve.******IMPACT. An experimentally-driven investigation of neuromorphic nanophotonics will serve as the first feasibility proof of using integrated photonics for scalable information processing. The proposed program has the potential to shape the emerging field of generalized compute engines beyond von-Neumann architectures and help redefine their physical limitations. The resulting technology has the potential to transform social, scientific, and technological sectors including self-navigating vehicles, bio-informatics, security, and big data. This research will contribute to the culture of innovation excellence across the scientific community and Canada. The multi-disciplinary nature of the program promises to foster collaborative ties across Canadian academic institutions and the private sector. The program's educational impact rests on uniquely positioning students for a readied workforce in academia or industry to drive tomorrow's advancements in photonics, applied physics, and engineering for 21st century challenges.

提要。计算机的诞生塑造了20世纪的社会和科学。在经历了几十年的指数级改进之后，冯·诺伊曼体系结构在速度、效率和通用性方面的性能已经开始达到基本极限，因为晶体管的缩小达到了它的物理极限。因此，当前的计算能力和计算需求之间的差距正在拉大。这一不足在涉及复杂系统、大数据或实时需求的问题上表现得很明显。由于金属互连在带宽和能源消耗方面的限制，非传统(非冯·诺伊曼)计算的尝试只取得了部分成功。拟议项目的基础工作源于我的班廷奖学金，它创造了神经形态光子学这个术语。我的跨学科研究的愿景是通过统一纳米光子(即光学)物理、神经形态(即大脑启发)架构和新兴技术(硅光子学)平台来促进对可扩展信息处理的光子物理学的理解。由此产生的光子处理器将有可能在能效和计算速度上分别比最先进的微电子处理器高出7个数量级和4个数量级。科学目标包括：1)采用基于石墨烯的电光调制器的推力能效(AtoJoule/操作)光子神经元；2)构建推力可扩展和可编程的硅光子神经网络结构；以及3)将推力-光子处理器应用于广义神经形态计算任务，包括深度学习和模型预测控制的非线性优化。这个程序专注于这些计算任务，因为它们是出了名的难以解决。一项由实验驱动的神经形态纳米光子学研究将作为使用集成光子学进行可扩展信息处理的第一个可行性证明。拟议的计划有可能超越冯-诺伊曼体系结构，塑造新兴的通用计算引擎领域，并帮助重新定义其物理限制。由此产生的技术有可能改变社会、科学和技术部门，包括自动导航车辆、生物信息学、安全和大数据。这项研究将有助于整个科学界和加拿大的创新卓越文化。该项目的多学科性质有望促进加拿大学术机构和私营部门之间的合作关系。该项目的教育影响取决于为学生在学术界或工业中准备好的劳动力提供独特的定位，以推动未来光子学、应用物理学和工程学的进步，以应对21世纪的挑战。